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Address black + isort fbsource linter warnings

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Summary: Address black + isort fbsource linter warnings from D20558374 (previous diff)

Reviewed By: nikhilaravi

Differential Revision: D20558373

fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8
This commit is contained in:
Patrick Labatut
2020-03-29 14:46:33 -07:00
committed by Facebook GitHub Bot
parent eb512ffde3
commit d57daa6f85
110 changed files with 705 additions and 1850 deletions
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27
tests/test_chamfer.py
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@@ -1,12 +1,11 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import unittest
import torch
import torch.nn.functional as F
from pytorch3d.loss import chamfer_distance
from common_testing import TestCaseMixin
from pytorch3d.loss import chamfer_distance
class TestChamfer(TestCaseMixin, unittest.TestCase):
@@ -19,14 +18,10 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
"""
device = torch.device("cuda:0")
p1 = torch.rand((batch_size, P1, 3), dtype=torch.float32, device=device)
p1_normals = torch.rand(
(batch_size, P1, 3), dtype=torch.float32, device=device
)
p1_normals = torch.rand((batch_size, P1, 3), dtype=torch.float32, device=device)
p1_normals = p1_normals / p1_normals.norm(dim=2, p=2, keepdim=True)
p2 = torch.rand((batch_size, P2, 3), dtype=torch.float32, device=device)
p2_normals = torch.rand(
(batch_size, P2, 3), dtype=torch.float32, device=device
)
p2_normals = torch.rand((batch_size, P2, 3), dtype=torch.float32, device=device)
p2_normals = p2_normals / p2_normals.norm(dim=2, p=2, keepdim=True)
weights = torch.rand((batch_size,), dtype=torch.float32, device=device)
@@ -47,9 +42,7 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
for n in range(N):
for i1 in range(P1):
for i2 in range(P2):
dist[n, i1, i2] = torch.sum(
(p1[n, i1, :] - p2[n, i2, :]) ** 2
)
dist[n, i1, i2] = torch.sum((p1[n, i1, :] - p2[n, i2, :]) ** 2)
loss = [
torch.min(dist, dim=2)[0], # (N, P1)
@@ -146,11 +139,7 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
# Error when point_reduction = "none" and batch_reduction = "none".
with self.assertRaises(ValueError):
chamfer_distance(
p1,
p2,
weights=weights,
batch_reduction="none",
point_reduction="none",
p1, p2, weights=weights, batch_reduction="none", point_reduction="none"
)
# Error when batch_reduction is not in ["none", "mean", "sum"].
@@ -339,9 +328,7 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
loss, loss_norm = chamfer_distance(p1, p2, weights=weights)
@staticmethod
def chamfer_with_init(
batch_size: int, P1: int, P2: int, return_normals: bool
):
def chamfer_with_init(batch_size: int, P1: int, P2: int, return_normals: bool):
p1, p2, p1_normals, p2_normals, weights = TestChamfer.init_pointclouds(
batch_size, P1, P2
)